diff --git a/transcribe.py b/transcribe.py
index 49c85db..b6db2b8 100644
--- a/transcribe.py
+++ b/transcribe.py
@@ -5,12 +5,12 @@ import queue
 import sys
 import torch
 from silero_vad import load_silero_vad, get_speech_timestamps
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from mlx_lm import load, generate
 
 # Parameters
 WHISPER_MODEL = "mlx-community/whisper-small.en-mlx"
-TRANSLATE_MODEL = "facebook/nllb-200-distilled-600M"
-TARGET_LANG = "spa_Latn" # Spanish (Latin America) - change as needed
+LLM_MODEL = "mlx-community/SmolLM2-135M-Instruct-4bit" # Tiny but fast LLM for translation
+TARGET_LANG = "Spanish"
 CHANNELS = 1
 SAMPLERATE = 16000
 BLOCK_SIZE = 512
@@ -25,19 +25,19 @@ def callback(indata, frames, time, status):
         print(status, file=sys.stderr)
     audio_queue.put(indata.copy())
 
-def main():
-    # Set device for translation model (using MPS for Mac M chips)
-    device = "mps" if torch.backends.mps.is_available() else "cpu"
-    print(f"Using device: {device}")
-
-    print(f"Loading Whisper model '{WHISPER_MODEL}'...")
-    # Whisper MLX runs on its own optimized path
+def translate_text(model, tokenizer, text, target_lang):
+    prompt = f"Translate the following English text to {target_lang}. Only provide the translation, no extra text.\n\nEnglish: {text}\n\n{target_lang}:"
+    messages = [{"role": "user", "content": prompt}]
+    prompt_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
     
-    print(f"Loading translation model '{TRANSLATE_MODEL}'...")
-    tokenizer = AutoTokenizer.from_pretrained(TRANSLATE_MODEL)
-    model = AutoModelForSeq2SeqLM.from_pretrained(TRANSLATE_MODEL).to(device)
-    translator = pipeline("translation", model=model, tokenizer=tokenizer, 
-                          src_lang="eng_Latn", tgt_lang=TARGET_LANG, device=device)
+    response = generate(model, tokenizer, prompt=prompt_text, max_tokens=100, verbose=False)
+    return response.strip()
+
+def main():
+    print(f"Loading Whisper model '{WHISPER_MODEL}'...")
+    
+    print(f"Loading LLM for translation '{LLM_MODEL}'...")
+    llm_model, llm_tokenizer = load(LLM_MODEL)
 
     print("Loading Silero VAD model...")
     vad_model = load_silero_vad()
@@ -80,12 +80,11 @@ def main():
                             original_text = result['text'].strip()
                             
                             if original_text:
-                                # 2. Translate
-                                translation = translator(original_text)
-                                translated_text = translation[0]['translation_text']
+                                # 2. Translate using LLM
+                                translated_text = translate_text(llm_model, llm_tokenizer, original_text, TARGET_LANG)
                                 
                                 print(f"\nEN: {original_text}")
-                                print(f"ES: {translated_text}")
+                                print(f"{TARGET_LANG[:2].upper()}: {translated_text}")
                             
                             audio_buffer = []
                             speech_started = False